Single-Cell Resolution IN Situ Hybridization On Tissues), a sensitive, multiplex RNA mapping approach. Direct hybridization of padlock probes on mRNA is followed by circularization with SplintR ligase and rolling circle amplification (RCA) of the hybridized padlock probes. Sequential detection of RCA-products using fluorophore-labeled oligonucleotides profiles thousands of cells in tissue sections. We evaluated SCRINSHOT specificity and sensitivity on murine and human organs. SCRINSHOT quantification of marker gene expression shows high correlation with published scRNA-Seq data over a broad range of gene expression levels. We demonstrate the utility of SCRINSHOT by mapping the locations of abundant and rare cell types along the murine airways. The amenability, multiplexity, and quantitative qualities of SCRINSHOT facilitate single-cell mRNA profiling of cell-state alterations in tissues under a variety of native and experimental conditions.Changes in cell identities and positions underlie tissue development and disease progression. Although single-cell mRNA sequencing (scRNA-Seq) methods rapidly generate extensive lists of cell states, spatially resolved single-cell mapping presents a challenging task. We developed SCRINSHOT ( This study presents SCRINSHOT, an amenable, multiplex RNA-mapping method, applicable to a wide variety of tissue types and conditions. It can function quantitatively across a broad range of expression levels and detect even rare cell types, facilitating the creation of digital tissue maps with single-cell resolution. Recent advances in single-cell RNA sequencing (scRNA-Seq) technologies enabled transcriptome analysis of individual cells and the identification of new cellular states in healthy and diseased conditions . These mTargeted methodologies are based on nucleic acid probes (mainly DNA), complementary to the RNA species of interest, as in all in situ hybridization assays . Single-Bacillus subtilis phage phi29 DNA polymerase (Φ29) polymerase-mediated rolling circle amplification (RCA) ):OCT at 4°C for 12–16 hours, with gentle rotation or shaking. Thereafter, tissues were embedded in OCT , using specific molds , and frozen in a slurry of isopentane and dry ice. Tissue-OCT blocks were kept at −80°C until sectioning. We cut 10-μm-thick sections with a cryostat (Leica CM3050S) and placed them on poly-lysine slides , kept at room temperature for 3 hours with silica gel  and then stored them at −80°C for further use.Use of human fetal material from elective routine abortions was approved by the Swedish National Board of Health and Welfare, and the analysis using this material was approved by the Swedish Ethical Review Authority (2018/769-31). After the clinical staff acquired informed written consent by the patient, the tissue retrieved was transferred to the research prenatal material. The lung sample was retrieved from a fetus at 8.5 weeks post-conception. The lung tissue was fixed for 8 hours at 4°C and processed as with mouse tissues.A detailed description of the padlock probe design is provided in the The 40–45 Taqman probe sequences were also used to prepare the fluorophore-labeled oligos. Using the IDT OligoAnalyzer tool, the length of the sequences was adjusted to Tm 56°C. To remove the fluorescent oligos after each detection cycle, we exchanged “T” nucleotides with “U” and treated them with Uracil-DNA Glycosylase . Detection oligos were labeled at their 3′-end with fluorophores and manufactured by Eurofins Genomics. The sequences of fluorophore-labeled oligos are provided in Slides were transferred from −80°C to 45°C to reduce moisture. A post-fixation step with 4% PFA in PBS 1x (pH 7.4) was done, followed by washes with PBS-Tween20 0.05%. Permeabilization of tissues was done with 0.1 M HCl for 3 minutes, followed by 2 washes with PBS Tween-20 0.05% and dehydration with a series of ethanol. SecureSeal hybridization chambers (Grace Bio-Labs) were mounted on the slides, and sections were preconditioned for 30 minutes at room temperature (R/T) with hybridization-reaction mixture of 1X Ampligase Buffer , 0.05 M KCl , 20% deionized Formamide , 0.2 μg/ul BSA , 1 U/μl Ribolock , and 0.2 μg/μl tRNA . To block unspecific binding of DNA, we included 0.1 μM Oligo-dT30 VN.Scgb1a1 mRNA, we used 0.01 μM of one padlock probe to minimize molecular and optical saturation. Hybridization included a denaturation step at 55°C for 15 minutes and an annealing step at 45°C for 2 hours. Not-hybridized padlock probes were removed by washes with 10% Formamide in 2X SSC . To minimize the effect of the previously documented SplintR ligase nucleotide preferences [Hybridization of the padlock probes was done in the pretreatment hybridization-reaction mixture solution, omitting Oligo-dT30 VN and adding each padlock probe to 0.05 μM final concentration. We used 3 padlock probes for every targeted RNA species. For the highly abundant ferences , 30, padRCA was done O/N at 30°C using 0.5 Units/μl Φ29 polymerase . The reaction mixture contained also 1X Φ29 buffer, 5% Glycerol, 0.25 mM dNTPs , 0.2 μg/μl BSA, and 0.1 μM RCA primers (RCA Primer1: TAAATAGACGCAGTCAGT*A*A and RCA Primer2: CGCAAGATATACG*T*C). The “*” indicate Thiophosphate-modified bounds to inhibit the 3–5 exonuclease activity of Φ29 polymerase [The visualization of the RCA products was done with hybridization of the 3′-fluorophore-labeled detection oligos. The reaction mixture contained 2X SSC, 20% deionized Formamide, 0.2 μg/μl BSA, 0.5 ng/μl DAPI , and 0.4 μM FITC-labeled and 0.2 μM Cy3- and Cy5-labeled detection probes for 1 hour at R/T. Washes were performed with 10% Formamide in 2X SSC, followed by 6X SSC. Tissues sections were dehydrated with a series of ethanol, chambers were removed, and the slides were covered with SlowFade Gold Antifade Mountant  and a coverslip.After image acquisition, the coverslips were removed by placing the slides in 70% ethanol in 45°C. Then, sections were dehydrated using a series of ethanol to mount the hybridization chambers. After tissue rehydration with PBS Tween-20 0.05%, the detection oligos were digested with Uracil-DNA Glycosylase for 1hour at 37°C. The reaction mixture contained 1X UNG buffer, 0.2 μg/μl BSA and 0.02 Units/μl Uracil-DNA Glycosylase . Destabilized oligos were stripped off by thorough washes with 65% deionized Formamide at 30°C. Multiple rounds of hybridization and imaging, as described in this section, were performed until all genes were imaged.Images were captured with a Zeiss Axio Observer Z.2 fluorescent microscope (Carl Zeiss Microscopy GmbH) with a Colibri led light source, equipped with a Zeiss AxioCam 506 Mono digital camera and an automated stage, set to detect the same regions after every hybridization cycle. For the confocal analysis of NEBs, we used a Zeiss LSM780 confocal microscope, equipped with Plan-Apochromat 63X/1.40 oil lens. Images were captured with optimal settings, according to Zeiss Zen Black software default settings.https://github.com/AlexSount/SCRINSHOT).The nuclear staining was used to align the images of the same areas between the hybridizations, and multichannel *.czi files, containing the images of all genes, were created using Zen2.5 (Carl Zeiss Microscopy GmbH). The images were analyzed as 16-bit *.tiff files, without compression or scaling. Images were tiled using a custom script in Matlab . Manual nuclear segmentation was done with Fiji ROI Manager . The nucA cell-ROI size criterion was applied to remove the outliers with very small or big surface. In particular, only cells included between 2 standard deviations of the mean size of the analyzed cells were further processed. In SCRINSHOT, to consider a cell ROI positive for an analyzed gene, we used a threshold strategy. First, we determined the maximum numbers of signal dots per cell ROI for all analyzed genes. A cell ROI was considered as positive if it contained more than 10% of the maximum number of signal dots for the specific gene. The higher threshold was set to 3, which was applied for highly abundant genes with maxima over 31 signal dots.Sftpc-CreERpos;Rosa26-Ai14pos lung, were used to visually inspect and remove the problematic cell ROIs from further analysis.In general, the 2-μm nuclear expansion provides an underestimation of real signal dots and provides satisfactory results for airway cells  values were used to calculate Euclidean distances and clustering was done using ward.D2 method. Balloon plots were created by ggpubr package in R [Annotated cells of submucosal gland, trachea, and lung airway epithelium were clustered using hclust package in R . Log2 and set it as a threshold. The RFPpos and RFPneg cell-ROIs were curated, and the Sftpc signal dots were measured in them.For the identification of the RFPRFP signal, the RFP protein fluorescence  of all segmented cell ROIs from the analyzed tissue sections was correlated with the detected RFP signal dots in the same cell ROIs, using simple linear regression analysis in GraphPad Prism with default settings.For the correlation of RFP protein fluorescence with SCRINSHOT Tissue sections were prepared, using the same protocol as SCRINSHOT. We used 1% BSA in PBS 1X (pH 7.4) with 0.2% Triton X100 (blocking buffer) to block unspecific protein binding and we incubated with primary antibodies O/N at 4°C. Slides were washed with PBS 1X (pH 7.4) 3 times for 5 minutes, and we incubated them with secondary antibodies in blocking buffer, when necessary, for 1 hour at room temperature. After three 10-minute washes with PBS 1X (pH 7.4), we counterstained nuclei with 0.5 ng/μl DAPI  and mounted with ProLong Diamond Antifade Mountant . We used the anti-proSftpc rabbit polyclonal antibody , the Alexa Fluor 488 anti-CC10 (E-11) mouse monoclonal antibody , the Alexa Fluor 555 anti-E-Cadherin mouse monoclonal antibody , and the Alexa Fluor 647 anti-CGRP mouse monoclonal antibody . For proSftpc visualization, we used the Alexa Fluor 488 donkey anti-rabbit secondary antibody .Scgb1a1, Sftpc) and intermediate (Actb) expressed genes, we used only one padlock probe and for Pecam1, 3 probes because of its lower expression levels.To compare the performance of padlock probe hybridization (1) directly on RNA (SCRINSHOT) and (2) after cDNA synthesis, we used sequential 10-μm-thick sections from adult mouse lungs, fixed for 8 hours . cDNA-based approaches in earlier publications have only used fresh frozen tissues and pepsin or proteinase K tissue treatments to increase RNA accessibility , 23. TheFor cDNA synthesis, the RNA species of the tissue sections were transformed to cDNA by RT using random decamers. The RNA strands were degraded with RNaseH to let padlock probes hybridize to the corresponding cDNA sequences. Padlock probe ligation was done with Ampligase DNA ligase. All other steps were done according to the provided SCRINSHOT protocol.For cDNA synthesis, we used 20 U/μl of the SuperScript II Reverse Transcriptase , 1X SuperScript II RT buffer (Thermo), 0.5 mM dNTPs , 10mers-random primer (Thermo), 0.2 μg/μl BSA , and 1 U/μl RiboLock RNase Inhibitor , at 42°C, O/N. The slides were post-fixed with 4% (w/v) PFA in PBS 1X (pH 7.4) at RT for 30 minutes, following by 6 washes with PBS-Tween 0.05%.Scgb1a1 padlock probes and 0.05 μM of the Actb. The “mismatch” probe had the same sequence as the normal Scgb1a1 padlock probe with a C>G substitution at the 5′- ligation site, and the “3’-scrambled” probe had the same 5′-arm, but the 3′-arm was scrambled. Actb padlock probe was used as internal control to calculate the Scgb1a1/Actb ratios. The Scgb1a1/Actb ratios of cell ROIs with zero Actb signal dots were considered as zeros. The detection of all Scgb1a1 RCA-products was done using a detection oligo, which recognizes the padlock backbone of Scgb1a1 but not of Actb. Actb RCA-product was detected by a detection oligo, which recognizes its gene-specific sequence  at different ratios: (1) P:A = 1:0, (2) P:A = 1:1, and (3) P:A = 1:5, keeping the Scgb1a1 padlock probe concentration to 0.05 μM.To compete the binding of Scgb1a1 padlock probe on its target transcript, an antisense competitor (A), which recognizes the Scgb1a1 mRNA between nucleotides 316–378 and masks the padlock probe hybridization site, was used in the same hybridization mixture with the 2(dots+1) transformed, and their mean values were calculated. Similarly, the SCRINSHOT signal dots per cell-ROI were log2(dots+1) transformed. Pearson correlation analysis was done using GraphPad Prism.The GSE118891 dataset was used to retrieve gene expression values (raw counts) of all AT2 and club cells, according to the cell annotation of provided metadata file . The genCftr sequencing reads, based on the original annotation of the dataset.The GSE103354 droplet dataset was analyzed with Seurat v3.1  package P ≤ 0.05, “**”: P ≤ 0.01, “***”: P ≤ 0.001, “****”: P ≤ 0.0001). For pairwise comparisons, the statistical analysis was done using Mann–Whitney nonparametric t test . Spearman correlation, with GraphPad Prism, was used to examine the correlation between SCRINSHOT and scRNA-Seq data. Spearman correlation, with “PerformanceAnalytic” R-package [All statistical analyses were done with GraphPad Prism, using nonparametric tests, because the SCRINSHOT data do not follow canonical distributions. Multiple comparisons were done using ANOVA Kruskal–Wallis multiple comparison test, without multiple comparison correction . All scripts are available at https://github.com/AlexSount/SCRINSHOT.The datasets and analysis files of the current study have been deposited at Zenodo repository  Bar-plots of the analyzed gene signals, in the indicated tissue compartments, for SCRINSHOT and cDNA-based approaches. The differences between the 2 conditions are significant (P < 0.0001) for all analyzed genes. (C) Histograms of the analyzed genes. The y-axes indicate the percentage of the cell ROIs and the x-axes, the binned signal dots in each cell. In SplintR-condition, 350 cells localized in arw and 1,624 in alv compartment. In cDNA-condition, there are 295 airway and 1,706 alveolar cells. Analysis was done using raw images, with the same acquisition conditions and thresholds. Only for visualization purposes, signal intensity of Scgb1a1 and Sftpc in cDNA-condition was set 5-times higher than SplintR. The data underlying this figure can be found in 10.5281/zenodo.3634561. alv, alveolar; arw, airway; cDNA, complementary DNA; PFA, paraformaldehyde-fixed; ROI, region of interest.(A) Images of SplintR-based (SCRINSHOT) and cDNA-based in situ hybridization assays. DAPI: blue, (TIF)Click here for additional data file.S2 FigCyp2f2 padlock probe. (‘A) indicates the area of square bracket in “A”. (B) SCRINSHOT signal dots of the mutated (C>T at the 3′-arm) Cyp2f2 padlock probe. (‘B) indicates the area of square bracket in “B”. The same Actb padlock probe was used in both conditions, as internal control, showing no statistically significant difference. DAPI: blue, Cyp2f2: red, Actb: gray. Scalebar: 10 μm. (C) Bar-plot of the SCRINSHOT dots per cell in all airway cells of the analyzed tissue sections (A and B). (D) Bar-plot of the Cyp2f2/Actb dot ratio in the control and mismatch conditions. (E) SCRINSHOT signal dots of the control Etv5 padlock probe. (F) SCRINSHOT signal dots of the mutated (T>C at the 5′-arm) Etv5 padlock probes. The same Actb padlock probe was used in both conditions, showing no statistically significant difference. DAPI: blue, Etv5: green, Actb: gray. Scalebar: 10 μm. (G) Bar-plot of the SCRINSHOT dots per cell, in all alveolar cells (E and F). (H) Bar-plot of the Etv5/Actb dot ratio in the control and mismatch conditions. Arrows indicate Etv5pos cells. The “n” values indicate the number of analyzed cells in each tissue section. The data underlying this figure can be found in 10.5281/zenodo.3978632. ns, not significant.(A) SCRINSHOT signal dots of the control (TIF)Click here for additional data file.S3 FigScgb1a1 SCRINSHOT dots. The arrows indicate areas with signal saturation, which are present only in distal airways. DAPI: blue, Scgb1a1: gray. Scalebar: 20 μm. (B) Violin plots of the average cell surface, as indicated by the 2-μm expanded nuclei and SCRINSHOT dot surfaces of Etv5  and Cyp2f2 (airway). Representative images of the recognized SCRINSHOT dots by CellProfiler custom script, that labels the identified dots according to the CellProfiler default colormap. The data underlying this figure can be found in 10.5281/zenodo.3978632.(A) Raw images of proximal (top) and distal (bottom) airway epithelium, showing the (TIF)Click here for additional data file.S4 FigPecam1 (magenta), Cldn5 (gray), and Cdh5 (yellow) were detected close to the lumen of the vessel, where this cell type is normally located. Acta2 (green) was detected at the inner thick part of the vessel wall, being consistent with the expression of the marker by vascular smooth muscle cells. Scalebar: 10 μm. Epithelial markers Sftpc (cyan) and Scgb1a1 (orange) were not detected. The data underlying this figure section can be found in 10.5281/zenodo.3978632. (B) Representative image from adult mouse kidney sections shows signal for Actb (gray), Napsa (magenta), and Lyz2 (cyan) but not Scgb1a1 (green) and Sftpc (red). Scalebar: 50 μm. DAPI (blue) was used for nuclear staining in both images. The data underlying this figure section can be found in 10.5281/zenodo.3634561.(A) Application of SCRINSHOT in adult mouse heart section, containing a vessel (v). The endothelial cell markers (TIF)Click here for additional data file.S5 FigSOX2 (green), SOX9 (red), and ASCL1 (gray). The square brackets correspond to the images on the right. DAPI (blue) was used as nuclear staining. Scale bar: 500 μm. (A) Representative image of proximal epithelium, which is highly positive of SOX2 and ASCL1 but not SOX9. (B) Representative image of highly SOX9 positive distal epithelium. The data underlying this figure can be found in 10.5281/zenodo.3634561.On the left, overview of a w8.5 whole left lung tissue section, showing SCRINSHOT signal for (TIF)Click here for additional data file.S6 FigAscl1pos (gray) neuroendocrine cells of an airway neuroepithelial body, in relation to Scgb1a1pos (red) club cells. DAPI: blue, scale bar: 10μm. (C) Note that neuroepithelial bodies are tightly packed cellular structures, as indicated by DAPI nuclear staining. The images on the right show the Calca RCA-products, detected in the first (D) and the eighth (E) detection cycles. (D’-F’) Magnified areas of the indicated positions (brackets) of images D–F, respectively. (F) Overlay of Calca identified signal dots in first (cyan) and eighth (yellow) detection cycles, using the same threshold in CellProfiler. The data underlying this Figure can be found in 10.5281/zenodo.3634561. RCA, rolling circle amplification. Images on the left show the (TIF)Click here for additional data file.S7 FigScgb1a1 and Ascl1. (Top) Maximal orthogonal projection of 20 z-stacks. (Bottom) Individual z-stacks. Cell outlines (magenta) were based on Scgb1a1 SCRINSHOT signal. DAPI: blue, Scgb1a1: yellow, Ascl1: gray. Scalebar: 5 μm. (B) Immunofluorescence of a representative NEB from an adult mouse lung. Club cells were stained with an anti-Scgb1a1 antibody and NE cells with an anti-CGRP antibody. Epithelial cell membrane was stained with an anti-E-cadherin antibody. (Top) Maximal orthogonal projection of 13 z-stacks. (Bottom) Individual z-stacks. Cell outlines, based on E-cadherin signal, are shown with magenta. Both images were acquired with optimal confocal microscopy settings, using a 63× oil-immersion lens. DAPI: blue, Scgb1a1: green, E-cadherin: red, CGRP: gray. Scalebar: 5 μm. The data underlying this Figure can be found in 10.5281/zenodo.3978632. CGRP, calcitonin gene-related peptide; NE, neuroendocrine; NEB, neuroepithelial body.Image of the same NEB in (TIF)Click here for additional data file.S8 Fign = 4,115 cells). The heatmap shows the log2(dots+1) SCRINSHOT-detected dots for the corresponding genes. Red insert shows 4 clustered ionocytes. (D) Analysis of the droplet-based scRNA-Seq dataset, using Seurat v3.1 [Cftr TPM counts of SmartSeq2 scRNA-Seq dataset [(A) Table showing the percentage of the cells of the indicated cell types, which express 1, 2, and 3 or more markers of the indicated cell type. Numbers in the parentheses show the number of used markers for each cell type. (B) Heatmap of the percentage of cells of the annotated cell types, that express one or more SCRINSHOT dots of the analyzed genes. (C) Hierarchical clustering of all analyzed cells with more than 3 dots of any gene and combination, without application of any cell size criteria Click here for additional data file.S9 FigEtv5 log2(counts+1) values (x-axis), in the GSE118891 annotated AT2 (black) and club (gray) cells. y-axis indicates percentage of cells. Club cells were used to set the positivity threshold for Etv5, because 95% of them are found below value 4 (outside the continuous red line). Distribution of Etv5 counts in AT2 cells was used to estimate the low-expressing cells that are found below one standard deviation (1.9) from Etv5 count average (9.4) (dotted red line). (B) Same type of analysis, as in “A”, for Etv5 SCRINSHOT dots in RFP fluorescence positive cells of the Sftpc-CreERpos;Rosa-Ai14pos lung section, shows that 96.5% of RFP-fluorpos cells have ≥1 Etv5 dots, and 14.8% of them are detected with low number of dots (below 1 standard deviation from Etv5 SCRINSHOT dot average). The data underlying this figure can be found in 10.5281/zenodo.3978632. RFP, red fluorescent protein; scRNA-Seq, single-cell RNA sequencing.(A) Histogram of SmartSeq2 (TIF)Click here for additional data file.S10 FigTfcp2l1 (green), Cftr (gray), Foxi1 (red), Ascl3 (yellow), and DAPI (blue). Scale bar: 500 μm. (Bottom) Magnified area of the indicated square in overview image, showing a detected ionocyte in submucosal gland. Scale bar: 10 μm. The data underlying this Figure can be found in 10.5281/zenodo.3634561.(Top) overview of the analyzed submucosal gland, showing SCRINSHOT signal for ionocyte markers, (TIF)Click here for additional data file.S11 FigMuc5b is detected along the airway epithelium of the proximal trachea, indicating that it is a general proximal epithelial cell marker. Tff2: green, Muc5b: red, Gp2: gray, Dcpp3: cyan, Dcpp1: magenta, Lipf: yellow, DAPI: blue, and cell ROIs: pink. Scale bar: 500 μm. (A) Insert showing the previously described Muc5bposTff2pos goblet subtype (arrow) and the LipfposDcpp3pos (asterisk). Single Lipfpos(hash) and Dcpp3pos (arrowhead) are detected in the same region, being positive for the general goblet cell marker Gp2. (B) Insert showing regionally restricted expression of Dcpp1 in a subset of Dcpp3pos cells. Insert scale bar: 10 μm. The data underlying this figure can be found in 10.5281/zenodo.3634561. ROI, region of interest.Overview of the analyzed submucosal gland for the expression of 6 goblet cell markers shows their expression in submucosal gland but not airway epithelium. (TIF)Click here for additional data file.S12 FigScgb1a1 (green), Sftpc (magenta), and Ascl1 (gray) of a large area from P21 Sftpc-CreERpos;Rosa-Ai14pos lung section, after Tamoxifen induction on P1 (RFP was not shown at that image). The image contains 14,167 manual segmented nuclei, which were expanded for 2 μm and considered as cells. (2) Spatial map of annotated cell types according to the indicated criteria. (A) Same airway area as in Scgb1a1: green and Cyp2f2: yellow) and NE-cell  markers, in an airway position with a neuro-epithelial body (NEB). (A’) Cell-type digital annotation of the area corresponding to “A”. The “*” indicate Ascl1posCalcaneg cells Club cells: green and NE-cells: red. (B) Same alveolar area as in Agerpos (gray) Sftpcneg (green) Cd74neg (magenta) AT1 cells (arrow), AgerlowSftpcposCd74low AT2 cells (asterisks), and AgernegSftpcnegLyz2posCd74high macrophages . (B’) Cell-type digital annotation of the area corresponding to “B”. AT1 cells: gray, AT2 cells: magenta and macrophages: white. All not annotated cells are depicted with blue. Scale bar: 200 μm. The data underlying this figure can be found in 10.5281/zenodo.3634561.(1) Overview image of SCRINSHOT fluorescence signal dots for (TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 TableRCA, rolling circle amplification.(XLSX)Click here for additional data file.S1 Text(PDF)Click here for additional data file. 31 Jan 2020Dear Dr Sountoulidis, Thank you for submitting your manuscript entitled "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections" for consideration as a Methods and Resources by PLOS Biology.Your manuscript has now been evaluated by the PLOS Biology editorial staff, as well as by an academic editor with relevant expertise, and I'm writing to let you know that we would like to send your submission out for external peer review.However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.Please re-submit your manuscript within two working days, i.e. by Feb 04 2020 11:59PM.https://www.editorialmanager.com/pbiologyLogin to Editorial Manager here: http://journals.plos.org/plosbiology/s/preprints for full details. If you consent to posting your current manuscript as a preprint, please upload a single Preprint PDF when you re-submit. During resubmission, you will be invited to opt-in to posting your pre-review manuscript as a bioRxiv preprint. Visit Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review. plosbiology@plos.org if you have any queries relating to your submission.Feel free to email us at Kind regards,Roli RobertsRoland G Roberts, PhD,Senior EditorPLOS Biology 28 Feb 2020Dear Dr Sountoulidis,Thank you very much for submitting your manuscript "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections" for consideration as a Methods and Resources at PLOS Biology. Your manuscript has been evaluated by the PLOS Biology editors, an Academic Editor with relevant expertise, and by three independent reviewers.The reviews of your manuscript are appended below. You will see that the reviewers find the work potentially interesting. However, while reviewers #2 and #3 are broadly positive, reviewer #1 is highly critical of your benchmarking and of the apparent performance of SCRINSHOT. We discussed these rather divergent reviews with the Academic Editor, and they were persuaded by reviewer #1 that the readers must be more thoroughly convinced of the merits of your method, by improved benchmarking against a more relevant suite of approaches. In addition, if the claimed advantages include ease of implementation and low cost, then these need to be more explicitly laid out and/or quantified.Based on reviewers' comments and following discussion with the Academic Editor, I regret that we cannot accept the current version of the manuscript for publication. We remain interested in your study and we would be willing to consider resubmission of a comprehensively revised version that thoroughly addresses all the reviewers' comments, and especially those of reviewer #1. We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript would be sent for further evaluation by the reviewers.We appreciate that these requests represent a great deal of extra work, and we are willing to relax our standard revision time to allow you six months to revise your manuscript.plosbiology@plos.org) if you have any questions or concerns, or would like to request an extension. At this stage, your manuscript remains formally under active consideration at our journal; please notify us by email if you do not intend to submit a revision so that we may end consideration of the manuscript at PLOS Biology.We expect to receive your revised manuscript within 6 months. Please email us  and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/*PLOS Data Policy*http://journals.plos.org/plosbiology/s/data-availability) requires that you make available all data used to draw the conclusions arrived at in your manuscript. If you have not already done so, you must include any data used in your manuscript either in appropriate repositories, within the body of the manuscript, or as supporting information . For an example see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5Please note that as a condition of publication PLOS' data policy  such that it can be cited independently in the future. For instructions see: Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Roli RobertsRoland G Roberts, PhD, Senior EditorPLOS Biology*****************************************************REVIEWERS' COMMENTS:Reviewer #1:In their manuscript, Sountoulidis and colleagues report a technology called Scrinshot, a spatial method for mapping single cell states in tissue. The main merit of this in situ hybridization technology is low cost and multiplexing capacity. However, the sensitivity is not well characterized, the specificity appears poor, and the overall performance of generating single cell profiles is very low. The dataset of mapped cells does not appear close to the purported sensitivity of cell identification based on representative images provided of different cell types, and the profiling approach cannot even be applied to tissues or regions where cells are not of uniformly cuboidal morphology, like lung alveoli. Finally, there is considerable manual manipulation with processing of the primary fluorescent data with a puncta thresholding approach that is not validated, and adjudication of autofluorescence is required, introducing the possibility of bias.Major criticisms1) The sensitivity is benchmarked against surfactant C which is perhaps the most highly expressed gene in the lung. Despite the massive expression levels of this transcript, no signal was detected in 1.1% of the genetically marked alveolar type II cells. While sensitivity was not assessed for moderate or lowly expressed genes, the images provided show very few puncta (on the order of 2 or 3) for many transcripts. For instance, in Supplemental Figure 2B, there is a large heart blood vessel which is only sparsely decorated with puncta for PECAM1.2) The authors provide some experiments showing that there is no signal when either the padlock cannot be ligated or the SplintR ligase is omitted. While these experiments demonstrate that a circle must be formed in order for signal to be produced, they do not substitute for control experiments showing that there is no significant off-target hybridization. The authors do perform a blocking oligonucleotide experiment for Secretoglobin 1a1 which has a nice result. However, when they actually quantify the specificity using surfactant C, this reveals a 7.4% false positive rate, which in my opinion is far too high to be acceptable for any staining technique. Supplemental Figure 4A-B encapsulate how poor the specificity of Scrinshot is, where most cells in a cluster that express the neuroendocrine marker Ascl1 (in panel A) co-express the Club cell marker Secretoglobin 1a1 (in panel B). Clearly, one of the markers is spuriously labeling the cells, since they should be complementary, rather than co-expressed. Another striking example is the type I marker AGER in Figure 6A for which puncta are shown in all epithelial cells, including most of the surfactant C positive cells which are putative type II cells. The authors seem to be aware of the extent of this specificity problem, because in their definitions of each cell type, they permit detection of one incompatible marker as long as there are two appropriate markers. This is highly contrived and betrays the high level of false positive signal produced by Scrinshot.3) The heat map in Figure 7C includes a very large number of airway cells, but close examination of the profiles reveals very poor performance of Scrinshot, presumably due to the addition of poor segmentation on top of the low specificity. The populations are mostly distinguished by very high expression of a single canonical marker, and it does not appear the other transcripts influence the clustering, since looking at the expression of most markers across the cell types shows very poor correlation with the cell type in which it is known to be expressed. For instance, multiple club cell markers are inappropriately detected in basal and NE cells . Also, apparently no ionocytes emerged (they are supposed to be coded blue in "Type" category) and CFTR expression is found scattered amongst cells belonging to most types.4) The authors cite many of the multiplexed in situ and spatial mapping technologies that have been published over the last few years, but rather than comparing the performance of Scrinshot against any of these, they present data benchmarking Scrinshot against cDNA-based in situ, an early technology that was abandoned because of its poor performance. All contemporary approaches to multiplexed in situ hybridization rely on RNA detection, for instance branched-chain (RNAscope) and the two most closely related to Scrinshot (StarMAP and PLISH) that also employ rolling circle amplification. So, if the authors want to demonstrate that Scrinshot is an advance for the field, they must compare its performance to a current standard in the field, not an early-generation technology that has been far surpassed.Reviewer #2:The authors present a spatial-transcriptomic technique named SCRINSHOT that aimed to address current limitations of spatial-transcriptomics. Long (40 nt) gene-specific padlock probes are hybridized to PFA-fixed sections using a stringent protocol, followed by SplintR ligation of bound probes and phi-29 rolling circle amplification (RCA) to improve the signal-to-noise ratio. RCA products were detected using fluorescent-labelled oligos that bound the gene part of the padlock probe. Multiple rounds of hybridization allowed for the detection of ~ 30 genes  in several thousand cells. The biological correlations used to validate SCRINSHOT are convincing and the level of detail in the protocol, from probe design to data analysis, is exemplary. Other techniques can detect more genes, some in 3D and at single molecule resolution, with examples of each referenced in the manuscripts introduction so this technique does not significantly outperform current state-of-the-art techniques in the field. However, the ease of SCRINSHOT implementation and lower equipment requirements will be of broad interest to labs that want to perform spatial-transcriptomics techniques and justifies publication in PlosBiology as a methods paper. However, I still have questions that need to be addressed before supporting acceptance of the manuscript.Comments● In my opinion the main novelty of this technique is ease of implementation. I suspect SCRINSHOT is cheaper compared to commercially available kits such as 10X Visium or RNAScope, providing a cost-effective way for labs to perform spatial transcriptomic. The authors echo this point in line 378; However, it is not sufficient to say SCRINSHOT is low cost without a detailed cost comparison vs commercially available spatial transcriptomics kits. The authors must include a detailed cost comparison so readers can decide if the reduced cost of SCRINSHOT justifies not using a commercial kit.● The spatial mapping of AT2 and AT1 cells in the distal lung, shown in Supplementary Figure 7, differs from work published by Tushar Desai's lab showing AT1 cells adjacent to AT2 cells using IF and lineage tracing (DOI: 10.1126/science.aam6603). The simplest explanation is SCRINSHOT is not highly efficient resulting in false negatives. A detailed explanation about this discrepancy is required.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5135277/pdf/jciinsight-1-90558.pdf)● No reference for Sftpc and Napsa as AT2 markers (e.g. ● Crowding of spots (proxy for a single RNA molecule) was described as an issue, yet in Figure 4 the number of dots was quantitated to perform correlation analysis with lineage-reporter expression with no explanation how crowding was overcome to allow such quantitation. Is crowding a gene specific issue (e.g. Scgb1a1)?● Line 158: Similar Actb expression in all slides was mentioned in the text but not shown.Reviewer #3:In this manuscript, the authors report a novel approach to multiplexed in situ mRNA detection, based on a combination of hybridization using padlock probes, SplintR mediated ligation, RCA amplification and detection using fluorochrome-conjugated oligonucleotides. This approach allows detection of up to 30 probes with a good dynamic range for gene expression levels of the detected transcripts on PFA-fixed tissue sections of human and mouse lung. The paper is generally wel written, and the method is a major advance over existing methodologies such as MERfish and In Situ Sequencing, and will be of importance to generate spatial maps of cell types and states when combined with scRNA-Seq or snRNA-Seq data, which is an urgent need for atlassing studies of healthy or diseased tissues or in model organisms.However, I also have some concerns and questions regarding some of the data, the analysis and the conclusions reached by the authors. In figure 2, the authors show data on the promiscuous ligation of the SplintR ligase, which might render aspecific signals. A single mismatch was introduced and shown to give strongly reduced signal, but not absence of signals. Given this effect, it would seem of relevance to explore this a bit further, and look at other mismatched probes , to allow a somewhat more systematic evaluation of the specificity of the technique.In figure 4 and 5 the authors evaluate the quantitative detection power and the cell-type specificity of the SCRINSHOT methodology, using the RosaAi14 reporter mouse backcrossed to a Sftpc-CreER train to induce recombination of the reporter in type-2 cells after tamoxifen application. However, these two figures use semi-overlapping methodology or even data and are quite confusing as presented currently. In Figure 4, RFP is used to identidy AT2 cells, whereas in Figure 5 this is performed by Sctpc detection using SCRINSHOT. In figure 4, the correlation between SCRNSHOT RFP mRNA detection and RFP fluorescence (protein levels) is shown to be quite good, although in the CreER negative mice, some background in this channel was caused by red blood cell autofluorescence. It remains unclear, however, whether all cells were analyzed, or only the cells at a specific cut-off for the RFP fluorescence signal. Also, for clarity the authors should not refer to the RFP (transgene) fluorescence as 'endogenous'. In Figure 5, the authors go on to analyze AT2 cells by using Sftpc expression using SCRINSHOT detection as a means of AT2 cell identification . This is initially confusing, so it could be better explained in the text on page 10. Just stating the relevant research question at the beginning of the paragraphs discussing the results presented in fig 4 and fig 5 would already be very helpful. In figure 5C, the authors use the same dataset but now define AT2 cells by RFP protein expression, and evaluate Sftpc expression by SCRINSHOT. Here, RFP+ Scftpc- cells are considered false negatives (for Sftpc expression) and RFP- Sftpc+ cells are considered false positives (for Sftpc expression). In Fig5D, his is reversed again . This seems - as also mentioned by the authors - not the best way to identify AT2 cells, as lineage tracing using genetic markers has a considerable false-positive and false negative rate itself. Rather, using another AT2 marker as presented in fig 6 might be a good way to estimate false-positive and false-negative rates . All in all ths figure and its data description is complex, confusing and in my opinion not the best analysis that could be done - some more evaluation of concordance between SCRINSHOT probes for AT2 cells might be helpful here, circumventing the use of the lineage trace. The conclusion of the authors should be about the eprformance of their technique, not of the lineage trace of the AT2 cells (which is now a significant confounding factor).In figure 6, a number of AT2 probes are used, which are tested for correlation to a published AT2 single-cell dataset. Expresion was detected in the RFP+ cells. The selected scRNA-seq dataset seems arbritrary and small (a few hundred cells sequenced) - why was this specific dataset used? In addition to correlation to scRNA-seq data it would be very helpful to see correlation of AT2 markers to each other when detected by SCRINSHOT in the same tissue section. If serial hybridizations were done to generate the data for figure 6 (so it seems looking at the figures), his could be analyzed by the authors, and these data would be highly relevant to evaluate the performance of SCRINSHOT. In figure 7 the authors present a true spatial map using multiplexed SCRINSHOT. These data are very interesting and will likely represent a highly relevant technological advance to the field. However, some technical data are lacking that are relevant for the performance of the methods. For instance, what is the overlap between the markers for the same cell type? How many cells per cell type show 1 2 or 3 markers for that cell type? How many cells are stained by markers for different cell types? How were these dealt with in the analysis? How do the fractions of cells compare to available data from classical histology or singe-cell RNAseq studies? These sort of analyses would add greatly to the significance of the paper, especially since 2555 cells fal into the category of unknown/ciliated. What exactly does this mean? These cells are for instance very frequently observed in submucosal glands - what cell type would these be? The detection of specific cell types is a great sset in this technique, but if the majority of the cells is not assigned to a known cell type, he application of the technique will of course be limited. the authors need to discuss these cells, and the limitations of their approach. If the main problem is cell segmentation during the analysis, this should not be specific for this technique and authors would need to clarify this or compare to similar methods also hampered by cell segmentation accuracy during analysis. All in all, this is a highly interesting and relevant paper, and I feel a great asset for this section in Plos Biology of the authors can address the abovementioned concerns. 12 Aug 2020AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 18 Sep 2020Dear Dr Sountoulidis,Thank you for submitting your revised Methods and Resources entitled "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections" for publication in PLOS Biology. I have now obtained advice from the original reviewers and have discussed their comments with the Academic Editor. We're delighted to let you know that we're now editorially satisfied with your manuscript. However before we can formally accept your paper and consider it "in press", we also need to ensure that your article conforms to our guidelines. A member of our team will be in touch shortly with a set of requests. As we can't proceed until these requirements are met, your swift response will help prevent delays to publication. Please also make sure to address the data and other policy-related requests noted at the end of this email.IMPORTANT:a) Many thanks for depositing the data underlying your Figures in Zenodo. Please could you clearly cite the Zenodo URL in the legends of all relevant main and supplementary Figures? e.g. "The data underlying this Figure can be found in 10.5281/zenodo.3634561"b) Please could you make your title more declarative and without punctuation? Perhaps "SCRINSHOT enables spatial mapping of cell states in tissue sections at single-cell resolution." I should say that we're not in love with the name "SCRINSHOT," so if you can think of something more euphonious that would be great!https://www.editorialmanager.com/pbiology/ and log in as an Author. Click the link labelled 'Submissions Needing Revision' to find your submission record. Your revised submission must include the following:To submit your revision, please go to - a cover letter that should detail your responses to any editorial requests, if applicable- a Response to Reviewers file that provides a detailed response to the reviewers' comments (if applicable)- a track-changes file indicating any changes that you have made to the manuscript. *Copyediting*Upon acceptance of your article, your final files will be copyedited and typeset into the final PDF. While you will have an opportunity to review these files as proofs, PLOS will only permit corrections to spelling or significant scientific errors. Therefore, please take this final revision time to assess and make any remaining major changes to your manuscript.NOTE: If Supporting Information files are included with your article, note that these are not copyedited and will be published as they are submitted. Please ensure that these files are legible and of high quality (at least 300 dpi) in an easily accessible file format. For this reason, please be aware that any references listed in an SI file will not be indexed. For more information, see our Supporting Information guidelines:https://journals.plos.org/plosbiology/s/supporting-information*Published Peer Review History*Please note that you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out. Please see here for more details:https://blogs.plos.org/plos/2019/05/plos-journals-now-open-for-published-peer-review/*Early Version*Please note that an uncorrected proof of your manuscript will be published online ahead of the final version, unless you opted out when submitting your manuscript. If, for any reason, you do not want an earlier version of your manuscript published online, uncheck the box. Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us as soon as possible if you or your institution is planning to press release the article.*Protocols deposition*https://journals.plos.org/plosbiology/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: Please do not hesitate to contact me should you have any questions.Sincerely,Roli RobertsRoland G Roberts, PhD,Senior Editor,rroberts@plos.org,PLOS Biology ------------------------------------------------------------------------BLOT AND GEL REPORTING REQUIREMENTS:https://journals.plos.org/plosbiology/s/figures#loc-blot-and-gel-reporting-requirementsFor manuscripts submitted on or after 1st July 2019, we require the original, uncropped and minimally adjusted images supporting all blot and gel results reported in an article's figures or Supporting Information files. We will require these files before a manuscript can be accepted so please prepare and upload them now. Please carefully read our guidelines for how to prepare and upload this data: ------------------------------------------------------------------------REVIEWERS' COMMENTS:Reviewer #1:After reviewing the revised manuscript and the authors' responses to my criticisms, I'm pleased to support publication in its current state.The additional experiments they performed have gone a long way towards alleviating my concerns about the sensitivity and specificity of their technology. Reviewer #2:We are happy with the authors' responses to our points now.Reviewer #3:The authors have gone to great lengths to address the issues raised in response to the first submission, and the manuscript has improved significantly as a result. All my major concerns have been accurately addressed. As a consequence of the review procedure and the careful revision, the inclusion of additional data and the careful discussion by the authors the manuscript now is quite long and offers a very detailed discussion. The manuscript is certainly acceptable in its current form, but might still benefit from a reread with the aim to improve legibility. 9 Oct 2020AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 13 Oct 2020Dear Dr Sountoulidis,On behalf of my colleagues and the Academic Editor, Emma Rawlins, I am pleased to inform you that we will be delighted to publish your Methods and Resources in PLOS Biology. The files will now enter our production system. You will receive a copyedited version of the manuscript, along with your figures for a final review. You will be given two business days to review and approve the copyedit. Then, within a week, you will receive a PDF proof of your typeset article. You will have two days to review the PDF and make any final corrections. If there is a chance that you'll be unavailable during the copy editing/proof review period, please provide us with contact details of one of the other authors whom you nominate to handle these stages on your behalf. This will ensure that any requested corrections reach the production department in time for publication.Early VersionThe version of your manuscript submitted at the copyedit stage will be posted online ahead of the final proof version, unless you have already opted out of the process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.PRESS biologypress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with http://www.plos.org/about/media-inquiries/embargo-policy/.We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit Thank you again for submitting your manuscript to PLOS Biology and for your support of Open Access publishing. Please do not hesitate to contact me if I can provide any assistance during the production process.Kind regards,Alice MussonPublishing Editor, PLOS Biologyon behalf ofRoland Roberts,Senior EditorPLOS Biology


In the present study, we used three strains of mice with various susceptibility to stress: mice with knock-out of the gene encoding norepinephrine transporter (NET-KO), which are well characterized as displaying a stress-resistant phenotype, as well as two strains of mice displaying two different stress-coping strategies, i.e., C57BL/6J (WT in the present study) and SWR/J. The procedure of restraint stress  was applied, and the following behavioral experiments (the forced swim test and sucrose preference test) indicated that NET-KO and SWR/J mice were less sensitive to RS than WT mice. Then, we aimed to find the miRNAs which changed in similar ways in the serum of NET-KO and SWR/J mice subjected to RS, being at the same time different from the miRNAs found in the serum of WT mice. Using Custom TaqMan Array MicroRNA Cards, with primers for majority of miRNAs expressed in the serum  allowed the identification of 21 such miRNAs. Our further analysis focused on miR-1 and miR-155 and their targets—these two miRNAs are involved in the regulation of BDNF expression and can be regarded as biomarkers of stress-resilience. Stress is defined as conditions that seriously perturb the psychological and physiological balance of an individual. However, the impact of stressful life events on physical and psychological well-being is highly variable, and does not affect everyone in the same manner; there are susceptible individuals who poorly adapt to stressors and express inappropriate responses that can become persistent states of stress, while resilient individuals can perceive adversity and develop adaptive psychological and physiological responses . The undThe idea of microRNAs (miRNAs) as mediators of the brain genomic response to stress is quite new, although recently many studies have been devoted to this subject, and various miRNAs or miRNAs families have been shown to be regulated in response to different kinds of stress ,4,5,6, aIn the present study, we used three strains of mice differentially reacting to stress: mice with knock-out of the gene encoding norepinephrine transporter (NET-KO), which are well characterized as displaying a stress-resistant phenotype ,8,9, as tm1Mca/IFPAS) males, ca. 4.5 month old, were used for the experiments. Genotypes were confirmed with a polymerase chain reaction (PCR) using the primers mNETEx2s (5′-GCT TTA TGG CAT GTA GTG TGC AC-3′), mNETEx2as (5′-GCT TTC TGC TTG AAC TTG AAG GC-3′), and EGFPas (5′-GCC GGA CAC GCT GAA CTT GTG-3′) to amplify 700 and 500 bp PCR products from WT and NET-KO mice, respectively.Heterozygous mice (C57BL/6J background), obtained from Dr M. Caron and generated by Xu and co-workers at Duke University Medical Center, Durham, NC, USA , were maThe swiss (SWR/J) strain was obtained from the Jackson Laboratory (USA). Six week old males and females were imported to IFPAS and mated to each other. The 4.5 month old males were used for the experiments.The mice were housed in groups. Animals had free access to food and water and were kept at a constant room temperature (24 °C) under a 12 h light/dark cycle. Animals were kept according to the decision of the Minister of Environment (no. 88/2014) and the Local Bioethic Commission (199/2017).The mice were housed individually one week before the experiment. Ten male mice per group were used for behavioral tests. After the adaptation period, they were placed in well-ventilated polypropylene tubes (diameter 28 mm and length 110 mm), located in their home cage, for 4 h. During the immobilization phase, the mice did not have access to food and water, and after the experiment they were left undisturbed in their home cage.The forced swim test was performed 6 days after the RS. Mice were individually placed in a transparent cylinder (20 cm in diameter) filled with water (23–25 °C) to a depth of 12 cm, to prevent them from using their tails to support themselves in the water. Immobility was measured during the last 4 min of a 6 min session. Immobility was defined as cessation of limb movements except for minor movements necessary to keep the mouse afloat .The sucrose preference test was performed 6 days after the RS. The experimental animals were acclimatized to two bottles of water one week before the RS; they were not deprived of food or water before the test. Then, they were given free choice between a 2% sucrose-solution and tap water for 24 h. The bottles with water and sucrose solution were replaced after 12 h of test time. The consumption of liquid was determined by weighting the bottles. The preference to sucrose was determined by comparison of sucrose-solution intake to the total amount of liquid drunk .t-test to compare the stressed group with the control group of each genotype. Additionally, control groups of NET-KO and SWR/J were compared with the control group of WT mice. The Student’s t-test was also used for these comparisons. The p values less than 0.05 were considered significant.Data from behavioral experiments were analyzed with GraphPad Prism 7.04. Each genotype was investigated during separate experiments, therefore data were analyzed by the Student’s The trunk blood was collected 10 min after the RS procedure from 7 male mice per group, as well as from mice not subjected to RS (control groups). Blood was left at room temperature for 20–30 min to clot. Then, it was centrifuged at 3000 rpm for 15 min at 4 °C. Serum was withdrawn and centrifuged again (3000 rpm for 5 min at 4 °C) to remove remaining cells. The hemolysis in serum was determined by the absorbance of hemoglobin at 414 nm using a NanoDrop ND-1000 . Serum samples with absorbance ≤ 0.3 were considered as unhemolyzed [9 copies to monitor RNA extraction [Total RNAs, including small RNAs, were isolated from 80 µL of the serum using miRNeasy Serum/Plasma Kit  according to the manufacturer’s instructions. We used 1 µg MS2 RNA as a carrier to increase recovery. The synthetic spiked-in ath-miR-159a was added to each sample at the amount of 3 × 10traction . The 400A preliminary experiment using the TaqMan Array Rodent MicroRNA A + B Cards Set v3.0  were placed on the Custom TaqMan Array MicroRNA Cards used for the main experiment. One Custom TaqMan Array MicroRNA Card was used per sample. The samples were ran in duplicates. The cDNA was synthetized by a TaqMan MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific) with a Megaplex RT Primers Rodent Pools Set v3.0—preliminary experiment—or with a TaqMan Custom RT Pool for further experiments, according to the manufacturer’s instructions. The obtained cDNA was preamplified to increase the quantity of desired cDNA using a TaqMan PreAmp Master Mix (Thermo Fisher Scientific) with Megaplex PreAmp Primers—preliminary experiment—or TaqMan Custom PreAmp Primers for further studies. The preamplified product was diluted and RT-qPCR reactions were performed with the TaqMan Universal PCR Master Mix, No AmpErase UNG (Thermo Fisher Scientific). The qPCRs were ran on a QuantStudio 12K Flex System .p ≤ 0.05 was considered to be significant.Data were further analyzed with the QuantStudio 12K Flex Software (Applied Biosystems). A Ct value above 30 was considered as undetectable miRNAs due to the preamplification. The same threshold equal to 0.2 was set for all samples for comparison. Then, the data were analyzed with qBasePLUS 3.1 software  which enables whole genome miRNA profiling with global mean normalization. The miRNAs like mmu-miR-301a-3p and mmu-miR-7a-1-3p—suitable for normalization—were generated by the geNorm algorithm ,18. Aftep ≤ 0.05 was considered to be significant and FDR correction was selected.The miRNAs with expression level significantly differentiating the experimental groups of animals were subjected to further bioinformatics analyses. The DIANA-miRPath v.3.0, an online software, was used for the assessment of miRNA regulatory roles and the identification of controlled pathways . FollowiAdditionally, the miRNet v1.0—the integrated platform linking miRNAs, targets and functions—was used to find a connection network of miRNAs and target genes regulated by them . Only geThe livers of four mice of each genotype under the control and stress conditions were separated 10 min after RS, frozen and stored at −80 °C until further use. The TRI Reagent  was used for RNA purification, according to manufacturer’s instructions. The liver was homogenized with 1 mL of reagent per sample, in the homogenizer stirrer . The amount of aqueous phase was always 400 µL. The 30 µL of RNase-free water was used for dissolved the RNA pellet. The quality and quantity of the isolated total RNA were evaluated by a NanoDrop ND-1000 (Thermo Fisher Scientific) and Experion microcapillary electrophoresis system . Samples that passed the quality threshold (RIN > 8.0) were used for further experiments.p-values ≤ 0.05 were considered statistically significant.The RNA was reverse transcribed to cDNA transcripts according to the manufacturer’s protocol, using a High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific). For RT-qPCR reactions, the TaqMan Universal Master Mix II (no UNG) and TaqMan Gene Expression Assay (Thermo Fisher Scientific) were added to the 20 ng of cDNA template. The list of TaqMan Gene Expression Assays was included in the p-values ≤ 0.05 were considered statistically significant.The trunk blood glucose level was measured in 8–9 male mice per group using a Diagnostic Gold glucometer , 10 min after RS. The statistical analysis was performed by one-way ANOVA with GraphPad Prism (Version 7.04) to determine the differences between groups. The Six groups of animals were studied: three different genotypes  were compared under control conditions and following restraint stress (RS). The forced swim test (FST) and sucrose preference test (SPT) were conducted in different cohorts of animals.Behavioral studies were performed in order to confirm the differences in vulnerability to stress of the three studied genotypes. Animals (control and subjected to RS) were exposed to the forced swim test (FST) and sucrose preference test (SPT), and the obtained results indicated that the response to stress varied between genotypes, and even a delayed effect of single RS was observed.As indicated by the FST, NET-KO and SWR/J mice displayed shorter immobility time than WT mice, which is usually interpreted as depression-resistant behavior, since a similar reaction is observed upon antidepressant drugs administration to WT animals . Single The preference for sucrose in all studied genotypes was not different either under the control conditions or 6 days after single RS B.The aim of our molecular studies was to find biomarkers for different behavioral responses to RS among miRNAs expressed in the serum of the three genotypes under study. For this reason, we first determined the miRNAs that are expressed in the serum of all three genotypes under the control and stress conditions. Out of 768 miRNAs present on both cards of the TaqMan Array Rodent MicroRNA, there were 146 miRNAs expressed on the A Card, while 56 miRNAs were expressed on the B Card. The list of miRNAs has been included in the (1) KEGG Pathway AnalysisThe eight miRNAs differentiated NET-KO from WT mice. Six of them were present in the TarBase v7.0 using KEGG pathway analysis with DIANA-miRPath v.3.0. These miRNAs were found to regulate genes associated with 21 pathways . The mos(2) Analysis of Clustered miRNAs Expressionp < 0.0001); miR-155  = 8.47; p = 0.001); miR-204  = 34.20; p < 0.0001).The miR-204, miR-155 and miR-1, which were clustered together, are mainly involved in these pathways’ regulation, and their expression was lower in the serum of NET-KO in comparison to WT mice B. The ef(3) Linking miRNAs to Target Genes—miRNet NetworkThe miRNAs differentiating NET-KO from WT mice were then linked to target genes (in such a way that they indicated genes regulated by at least two miRNAs), which resulted in the network linking all six miRNAs with the relevant genes. Among them were: Hdac4, Rheb, Ets1, Mef2a regulated by miR-1 and miR-155, as well as Gsk3b regulated by miR-155 and miR-195 C.(1) KEGG Pathway AnalysisOn the other hand, there were 25 miRNAs which differentiated SWR/J from WT mice. One miRNA was not found in the TarBase v7.0. The remaining miRNAs were clustered into three groups. One of them showed a more significant relationship to the KEGG pathways. This group included miR-1, miR-20, miR-106, miR-155, miR-93 and miR-25. The miR-204 also differentiated SWR/J from WT mice, but it was positioned in another cluster. The following signaling pathways are regulated by these miRNAs: fatty acid biosynthesis, neurotrophin, TGF-beta, thyroid hormone, and insulin as well as lysine degradation, endocytosis and MAPK signaling pathway .(2) Analysis of Clustered miRNAs Expressionp < 0.0001); miR-204  = 34.20; p < 0.0001); miR-155  = 8.47; p = 0.001); miR-20  = 27.57; p < 0.0001); miR-106  = 17.52; p < 0.0001); miR-93  = 40.68; p < 0.0001); miR-25  = 48.42; p < 0.0001).The expression of miR-1, miR-204 and miR-155 was decreased in the serum of SWR/J mice as compared to the control group of WT animals. However, the expression of the remaining miRNAs was higher when SWR/J mice were compared to WT control group E. The ef(3) Linking miRNAs to Target Genes—miRNet NetworkThe linking miRNAs which differentiated SWR/J from WT mice with the target genes (regulated by at least two of them) resulted in the network F, includThe behavioral experiments described above indicated that NET-KO and SWR/J mice were less sensitive to RS than WT mice. Therefore, this part of the study aimed to find the miRNAs which changed in similar ways in the serum of NET-KO and SWR/J mice subjected to RS, being at the same time different from the miRNAs found in the serum of WT mice.We were able to identify 21 miRNAs that changed in similar ways in the serum of NET-KO and SWR/J mice subjected to RS. Three of them were not found in TarBase v7.0. The remaining ones are described as affecting the regulation of pathways related to biosynthesis, metabolism, elongation and degradation of fatty acids; steroid biosynthesis; endocytosis and lysine degradation, as well as the thyroid hormone signaling pathway . The miRp < 0.0001); miR-155  = 22.88; p < 0.0001); miR-324  = 22.63; p < 0.0001); miR-30  = 17.93; p = 0.0002); miR-19  = 23.89; p < 0.0001); miR-146  = 50.44; p < 0.0001); miR-200  = 27.95; p < 0.0001); miR-214  = 85.43; p < 0.0001).The alterations in expression of miRNAs mentioned above under RS conditions are shown in The miRNAs differentiating the stress response of NET-KO and SWR/J mice from WT animals were connected in the network if mRNA was regulated by more than one miRNA. This network included 16 miRNAs and 148 mRNAs C. The liOur further analysis focused on miR-1, because it differentiated control groups of NET-KO as well as SWR/J from WT mice, and also it differentiated the stress response of NET-KO and SWR/J mice from WT animals. Analyses described above pointed to the following mRNAs regulated by miR-1: Igf1, Igf1r, Hdac4, Mef2a, Anxa5, Rheb, Ets1, and Gja1. The miR-1 connected network is shown in The expression of some genes, indicated by miRNet analysis as regulated by miR-1, was checked in the liver. Additionally, BDNF and Ntrk2 were also selected, based on the available literature.p < 0.0001), while the effect of genotype was insignificant  = 0.045; p = 0.95). In turn, expression of mRNA encoding Ntrk2 was significantly higher in the WT mice as compared to the control group of NET-KO and SWR/J mice, and it was reduced following RS in the WT mice, while it did not change in the liver of NET-KO and SWR/J mice after stress (p < 0.0001), and the effect of genotype was also significant  = 30.64; p < 0.0001). In the SWR/J mice, a significantly higher level of Igf1 mRNA was observed in comparison with both WT and NET-KO mice, and no differences in this mRNA level were observed following RS in all genotypes (p < 0.0001), while the effect of stress was insignificant  = 0.39; p = 0.53). As far as the expression of mRNA encoding for Igf1 receptor (Igf1r)—no differences were observed between the studied genotypes both under the control and stress conditions  of the control NET-KO mice was significantly reduced compared with WT control animals. This result correlated with previously published data ,8,12,21.Since it has been postulated that the stress response may be associated with miRNAs present in the serum, we decided to look for markers of stress-resilience in three genotypes of mice responding differently to RS. Therefore, our preliminary molecular studies identified miRNAs, which were expressed in the serum of all three groups of mice. Most of the miRNAs detected in the present study have been also identified by Mi and co-workers . FurtherAmong miRNAs that differentiated the response to stress of NET-KO and SWR/J mice from WT animals were miR-1 and miR-155, mentioned earlier, but also miR-324, miR-30, miR-19, miR-146, miR-200 and miR-214. The changes in miR-324 and miR-19 expression occurred only in the serum of WT mice under stress conditions, while in other genotypes they did not differ. This effect is particularly interesting, since these miRNAs have been shown as associated with post-traumatic stress disorder (PTSD): the studies by Balakathiresan and colleagues on the animal model of PTSD identified miR-324 and miR-19 as differentially expressed when compared with the serum of control animals, which correlated with the expression of these miRNAs in the amygdala . AnotherAll the miRNAs mentioned above are interesting in the context of resilience to RS, however, the most interesting results obtained in the present study concern miR-1. The level of this miRNA was lower in the serum of control groups of NET-KO and SWR/J, and it decreased in WT mice upon RS, while it did not change in other two genotypes. The miR-1 has been associated with cardiac dysfunction, immunology , skeletal muscle changes , but also as a regulator of glucose metabolism in the liver ,43,44,45In conclusion, our research indicates that the varied response to stress depended on the genotype, and the serum level of miR-1 and miR-155, which are involved in regulation of BDNF expression, can be regarded as biomarkers of stress-resilience.


To survey the health-related quality of life (HRQoL) and its influencing factors among patients with COVID-19 in their first medical follow up.All patients diagnosed with COVID-19 were discharged from 12 hospitals in Wenzhou, Zhejiang from Jan 17, 2020 to Mar 20, 2020. Prospectively collected and analyzed data included demographics, clinical symptoms, comorbidity, and chest CT imaging features at the first follow up, 1 month after discharge. All patients underwent the HRQoL evaluation with the Chinese version of Short-Form 36-item questionnaire (SF-36) as well as a general condition questionnaire. Factors associated with SF-36 were constructed using linear regression. Predictors of impaired physical component summary (PCS) and a mental component summary (MCS) were identified by logistic regression.p<0.05). The multiple linear regressions demonstrated that age was negatively associated with PF, role physical (RP), but positively associated with vitality (VT) (p<0.05). PF, bodily pain (BP), and role-emotional (RE) were negatively associated with the female sex (p<0.05). For mental health, the clinical subtypes were significant associated factors (p < 0.05). Length of stay (LOS) was strongly negatively associated with RE and RP, and positively associated with VT (p< 0.05). Logistical regression revealed that non-obese overweight (OR 3.71) and obesity (OR 3.94) were risk factors for a low PCS and female sex (OR 2.22) was a risk factor for a low MCS.SF-36 demonstrated a significant difference in HRQoL in patients with COVID-19, except in physical function (PF), when compared to the general Chinese population (Health-related quality of life was poor among COVID-19 patients at the 1 month follow-up. Patients suffered from significant physical and psychological impairment. Therefore, prospective monitoring of individuals exposed to SARS-CoV-2 is needed in order to fully understand the long-term impact of COVID-19, as well as to inform prompt and efficient interventions to alleviate suffering. The coronavirus disease 19 (COVID-19) is an infectious disease caused by the relentless spread of the severe acute respiratory coronavirus 2 (SARS-CoV-2) from human to human, all across the world , and othAlong with social progress and the transformation of medical care and service systems, interest in health-related quality of life (HRQoL) is increasing . HRQoL iWenzhou is located in the southeastern coast of China, which has a population of 9.3 million. Wenzhou was initially one of the worst hit cities out of Hubei Province with 504 confirmed cases due to the highest volume of mobility with Wuhan , 13. TheThis is a multicenter and cross-sectional study of patients with COVID-19 who were discharged from Jan 17, 2020 to Mar 20, 2020 at first month follow up from twelve hospital isolation wards in Wenzhou City, Zhejiang Province, China. The diagnosis of COVID-19 was based on the Chinese standard at the time . All patThe questionnaire contains questions with defined response categories. A few questions asked participants to provide descriptive information. Participants were informed of the purpose, the agency conducting the research, and the privacy protection of survey. The study was approved by the Institutional Review Board of Wenzhou Medical University. Written informed consent was obtained from all participants.The Chinese version of the SF-36 was translated from the International Quality of Life Assessment (IQOLA) SF-36 Standard UK Version 1.0 , composeThe Chinese population norm was based on the study done by He and colleagues . A randop-value threshold of <0.05 was considered statistically significant. All statistical analyses were performed using SPSS software .Descriptive statistics for demographic information were calculated. The results were expressed as either the mean ± standard deviation (SD) or the categorical data were summarized as percentage of the total group. Differences in quantitative data distributions between patient subgroups were tested by Student’s t-test for normally distributed data and by Wilcoxon rank-sum test or Kruskal-Wallis test for non-normally distributed data. Linear regression analysis was performed to explore the correlation between two variables. Logistic regression analysis was used to determine factors associated with decreased PCS score and MCS score. A Five hundred and four COVID-19 patients were enrolled in this cohort study. Among the 503 survivors, 131 did not follow up and 11 provided incomplete data. A total of 361 participants were available for analysis. Baseline characteristics of the participants were collected (p<0.05). However, the scores of BP, GH, VT, and MH were higher than the norm group (p<0.05). Furthermore, there was no difference between two groups in PF score (p=0.75). At baseline, the mean scores were 55.96 ± 7.24 points for the PCSs and 48.92 ± 10.81 points for the MCSs, respectively. Comparison of HRQoL outcomes between COVID-19 patients and subjects with normal health of the different sexes was performed (p<0.01). In contrast, the scores of BP, MH, GH, and VT were higher than the normal group (p<0.01). However, no significant differences were observed between the two groups in PF .The SF-36 mean score for eight specific dimensions was measured (p < 0.05). PF, BP, and RE were negatively associated with the female sex (p< 0.05). The severity of the clinical subtype was significantly negatively associated with the PF, GH, RE, and MH (p < 0.05). Length of stay (LOS) was negatively associated with RE and RP, and positively associated with VT (p < 0.05). In addition, there were significant negative association between lung function parameters  and MH (P < 0.05).Multivariate linear regression analysis was used to identify factors related to HRQoL of the follow-up cohort  in nearly every published study to date . In our This study has several limitations. (1) This study may be biased due to relatively mild disease. Milder illness may correlate with higher quality of life and cause an overestimate of HRQoL. (2) Another limitation is that the study population did not include children, which should be investigated further in future research. (3) Furthermore, investigation of the physical and mental health of COVID-19 patients should include more specific, comprehensive evaluation tools, such as the Quality of Life Enjoyment and Satisfaction Questionnaire, Hamilton Anxiety Scale, and Hamilton Depression Scale, which may add to the accuracy of assessment of mental health status. (4) Finally, the cross-sectional nature of the data precludes making causal inferences.Our study provides a database for the physical profile, psychological profile, and HRQoL status of patients with COVID-19 at first month follow up. The HRQoL impairment of Chinese COVID-19 patients was significant. We propose early measures should be taken to prevent mental health problems, as well as initiation of a comprehensive program to assist COVID-19 patients in recovering basic function. Furthermore, we encourage the biomedical research community to pursue longitudinal monitoring of neuropsychiatric symptoms and status. Further follow-up is needed to assess the HRQoL of COVID-19 patients.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Wenzhou Medical University. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.K-YC, J-SZ, and X-KL conceived the study. K-YC, TL, and F-HG collected and analyzed the data. K-YC, J-SZ, and X-KL wrote the paper. All authors contributed to the article and approved the submitted version.The study was partially supported by grants from China Academy of Engineering 2020-XY-88(2020-KYGG-04-02) and COVID-19 Emergency Response projects (No. ZY202002 and No. 2020-004).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Whether the change of the pre- and postoperative systemic inflammatory response (SIR) levels will affect the prognosis of gastric cancer (GC) is unclear. We aimed to investigate the dynamic changes in the pre- and postoperative SIR and their prognostic value for GC.The clinicopathological data from 2257 patients who underwent radical gastrectomy between January 2009 and December 2014 at Fujian Medical University Union Hospital (FMUUH) were analyzed. Perioperative SIR changes were reported as changes in the lymphocyte-monocyte ratio (LMR), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII).P = 0.024). The postoperative 12-month (post-12-month) LMR predicted the 5-year overall survival (OS) rate with the highest accuracy (areas under the curve [AUC] 0.717). Patients were divided into four groups according to the optimal cutoff of the preoperative and post-12-month LMR: high pre-LMR to high postoperative (post)-LMR group, high pre-LMR to low post-LMR group, low pre-LMR to high post-LMR group, and low pre-LMR to low post-LMR group. The survival analysis showed 5-year OS rate was significantly higher in patients with high post-12-month LMR than in patients with low post-12-month LMR, regardless of pre-LMR levels . The prognostic accuracy was significantly improved by incorporating the post-12-month LMR in the tumor-node-metastasis (TNM) staging system (P = 0.003).The SIR levels showed different trends from postoperative months 1 to 12. Multivariate analysis showed that preoperative (pre)-LMR was an independent predictor for the prognosis  contains supplementary material, which is available to authorized users. In recent years, some studies have explored the relationship between different inflammatory markers and the prognosis of patients with GC.10 The lymphocyte-to-monocyte ratio (LMR), neutrophil-to-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) are the most easily obtained inflammatory markers and can be obtained from complete blood count (CBC) testing, which is a convenient method for dynamic preoperative and postoperative repeated measurements.14 As potential markers for predicting the prognosis and guiding the treatment of patients with GC, they reflect the complex interactions between the local immune response and systemic inflammatory response (SIR) in the tumor microenvironment.15 However, previous studies have mostly been limited to exploring the relationship between the preoperative (pre)-SIR and the prognosis. Following surgery and adjuvant chemotherapy, postoperative (post)-SIR levels may differ from preoperative levels. In this study, we aimed to longitudinally investigate and characterize the SIR from the preoperative period through multiple time points in the postoperative period to examine the dynamic changes in perioperative LMR, NLR, PLR, and SII and to investigate whether changes in the SIR would confer a difference in the overall survival (OS).Gastric cancer (GC) is one of the most common malignancies of the digestive system; it is the 5th most common malignancy and the 3rd leading cause of cancer-related death.16 Six to eight cycles of adjuvant chemotherapy using 5-fluorouracil (5-FU)-based regimens  were recommended for the majority of patients with advanced GC. The postoperative pathological stage of the tumor was determined according to the eighth edition of the American Joint Committee on Cancer (AJCC) and Union for International Cancer Control (UICC) staging manual.17This study retrospectively analyzed the clinicopathological data from patients undergoing radical gastrectomy at Fujian Medical University Union Hospital (FMUUH) from January 2009 to December 2014. The following inclusion criteria were established: (1) number of examined nodes > 15 and (2) no evidence of distant metastasis. Patients were excluded if they (1) had received neoadjuvant therapy, (2) had not undergone a routine blood examination before surgery, or (3) had incomplete/inaccurate clinical or pathological information. Finally, 2257 patients were included in this study for the baseline analysis  (defined as post-1-month), 46th to 75th POD (defined as post-2-month), 76th to 105th POD (defined as post-3-month), 106th to 135th POD (defined as post-4-month), 136th to 165th POD (defined as post-5-month), 166th to 270th POD (defined as post-6-month), and 271st POD and later (defined as post-12-month). If multiple blood test results were available in the same period, the result closest to each time point after surgery was recorded. For example, if one patient underwent postoperative blood tests at POD 342 and POD 390, the results from the test performed at POD 342 were recorded, because these results were closest to post-12-month (POD 360). Because of differences in physician follow-up preferences, differences in the tumor stages of patients in the cohort, and differences in patient compliance, not all patients underwent blood testing at each of these time points. The preoperative value of CBC-based markers of this study refers to the preoperative average value of the population with blood available at a specific postoperative time point, but not the average value of the baseline population. For example, there were 337 patients with blood available at post-6-month, the pre-LMR value at this time point is the average value of the pre-LMR of 337 patients, and at post-12-month, 157 patients underwent postoperative blood tests, and the pre-LMR value at this time point was the average LMR value of 157 patients. This ensures the consistency of the population before and after surgery at the corresponding time points, which may help to compare pre- and postoperative changes as well as to reduce errors. Candidate CBC-based inflammatory markers in our study included the LMR, NLR, PLR, and SII (platelets*NLR).http://medicine.yale.edu/lab/rimm/research/software.aspx) was used to determine the optimal cutoff values of candidate CBC-based inflammatory markers for OS. X-tile plots present a new tool for the assessment of biological relationships between a biomarker and outcome and the discovery of population cut-points based on marker expression. A population is divided into different divisions based on every possible cutoff point. All possible divisions of the cutoff point are statistically assessed. Then, X-tile plots calculate χ2 values for every possible division of the population. The optimal cutoff value for survival was calculated by selecting the minimum P value with the maximum χ2 value.18 The cutoff values for the pre-LMR, NLR, PLR, and SII were 3.42, 3.87, 162.50, and 569.93, respectively, and the cutoff value of post-12-month LMR was 4.00, as determined by the X-tile software.The X-tile program  scans that were repeated every 3–6 months for 2 years, every 6–12 months from years 2 to 5, then annually thereafter. The survival time was recorded from the date of surgery to the last follow-up date, date of death, or date at the end of follow-up in the database (such as loss to follow-up or death due to other diseases).t test was used to determine the statistical significance of changes in the preoperative and postoperative levels of inflammatory markers at each time point. Both the log-rank test and multivariate Cox proportional hazards regression models were used to analyze the relationship between serum inflammatory markers and OS. Variables that were statistically significant (P < 0.05) in the univariate analysis were entered into a multivariate Cox regression model. Survival estimates were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). Areas under the curves (AUCs) were calculated to estimate the prognostic abilities of the preoperative and postoperative inflammatory markers as risk factors for OS. Statistical analyses were performed using SPSS 22.0 software  and R 3.6.0 software . All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant.Descriptive statistics were used to summarize cohort characteristics, and continuous variables are reported as means ± standard deviation (SD). The paired-sample 2. In terms of disease characteristics, the majority of the tumors were located in the lower third of the stomach (42.1%), with a mean tumor size of 45.2 ± 25.1 mm. Total gastrectomy was performed in 1180 cases (52.3%), distal gastrectomy was performed in 1027 cases (45.5%), and proximal gastrectomy was performed in 49 cases (2.2%). Most patients were diagnosed with the undifferentiated histological type (77.1%), without vascular invasion (77.1%) or perineural invasion (83.6%). The distribution of TNM stages were as follows: 660 (29.2%) patients with stage I, 553 (24.5%) with stage II, and 1044 (46.3%) with stage III disease. More than half (52.7%) of the patients received postoperative adjuvant chemotherapy (485 patients lacked records of postoperative adjuvant chemotherapy). Regarding the CBC-based inflammatory markers, the mean LMR, NLR, PLR, and SII were 4.5 ± 2.1, 2.6 ± 2.2, 154.2 ± 79.1, and 636.6 ± 593.5, respectively. The baseline characteristics of this cohort are documented in Supplementary Table Of the 2257 patients with GC included in the study, 1698 (75.2%) were male and 559 (24.8%) were female, and the mean age was 60.9 ± 11.2 years. The majority had an American Society of Anesthesiologists (ASA) score of 1, with a mean body mass index (BMI) of 22.8 ± 8.8 kg/mP < 0.05). The post-NLR, PLR, and SII also showed different trends over time respectively, but at post-12 month, only LMR was significantly higher than the preoperative values . In addition, other variables, including age, BMI, ASA score, tumor location, tumor size, histological type, vascular invasion, perineural invasion, pathological tumor-node-metastasis (pTNM) stage, and adjuvant chemotherapy, were also associated with OS . In multivariate analyses, only the pre-LMR (P = 0.024) was an independent CBC-based inflammatory marker for OS. Other independent variables, including age, BMI, tumor location, tumor size, pTNM stage, and adjuvant chemotherapy, were associated with OS  (Table The median follow-up time of the entire cohort was 65.6 months (range from 1 to 117 months), with a 5-year OS rate of 67.5%. According to the univariate analysis, the four CBC-based inflammatory markers were all associated with OS , tumor location (P = 0.037), tumor size (P = 0.003), vascular invasion (P = 0.003), pTNM stage (P < 0.001), adjuvant chemotherapy (P = 0.004), and LMR change group (P < 0.001) were significant. In the multivariate analysis, tumor location (P = 0.009), pTNM stage (P < 0.001), and LMR change group (P = 0.008) remained significant. Examining the changes in the LMR, patients in H-L group had a HR of 2.945 (95% CI 1.308–6.646) compared to the referent H-H group. The 5-year OS rate for the former group was 43.2% compared with 81.6% for the latter group. Patients in L-H group had an HR of 1.239 (95% CI 0.378–4.056), with a 5-year OS rate of 81.8%. Patients in L-L group had an HR of 3.682 (95% CI 1.607–8.436), with a 5-year OS rate of 45.8% (Table P < 0.001)  Fig. .Table 2UP < 0.05). But there was no statistical difference in locoregional recurrence between the two groups of patients  , and post-12-month carbohydrate antigen 19-9 (CA-199) levels . Further stratified analysis based on pTNM stage showed that there was no statistical difference in post-12-month LMR between those who received adjuvant chemotherapy and those who did not in patients with the same stage .We utilized the iAUC box plot to represent the AUC with CIs, including pT stage, pN stage, pTNM stage, and post-12-month LMR. As shown in Fig. P = 0.007). A pre-LMR ≥ 3.42 was an independent protective factor for the prognosis in patients with GC . The post-12-month LMR displayed the greatest accuracy in predicting the 5-year OS rate . A survival analysis combing preoperative and post-12-month LMRs showed that the 5-year OS rate of patients with an LMR ≥ 4.00 was higher than patients with an LMR < 4.00 regardless of pre-LMR level. The incorporation of the post-12-month LMR into the TNM staging system significantly improved the accuracy of the prognosis (P = 0.003).This study is the first to show the dynamic changes in the preoperative and post-SIR in patients with GC by performing a retrospective analysis of a large population. The NLR, PLR, and SII in GC returned to preoperative levels at post-12-month, while post-12-month LMR was still significantly higher than preoperative  is an independent predictor of OS in patients with GC. Badgwell et al. found that the risk of death with R1 resection was 2.29 comparing to R0 resection .28 Because the prognosis of patients with R1 and R0 resection is very different, we only included patients with R0 resection in order to investigate the changes of pre- and postoperative SIR and their prognosis values for GC patients received radical gastrectomy.Inflammation is one of the seven characteristics of cancer, causing approximately 25% of new cancer cases worldwide.29; therefore, the consistently higher post-LMR than the preoperative value may reflect long-term chronic inflammatory processes that do not easily change with the termination of cancer treatment. As shown in our study, neutrophils and platelets appear to be more sensitive to chemotherapy, and these two cell types are often used as indicators to assess the patient’s tolerance to chemotherapy, which is also confirmed by our results. However, a clear understanding of the mechanism underlying these differences was beyond the scope of the current study.This study reported the changes in perioperative SIR levels in patients with GC by describing the dynamic changes in LMR, NLR, PLR, and SII recorded at preoperative and postoperative time points. The LMR and NLR were higher at each time point after surgery than at the preoperative assessment, while PLR and SII were lower than the preoperative values. A potential explanation for these findings is that radical surgery eliminates most of the tumor burden along with the hematological effects of chemotherapy. At 12 months after surgery, the pre- and postoperative differences in NLR, PLR, and SII were no longer statistically significant with the end of adjuvant chemotherapy, and the LMR was still significantly higher than the preoperative value. In a previous study, mononuclear cells were described as the key to maintaining chronic inflammatory processes30 The difference in the optimal threshold of hematological parameters before and after surgery may be caused by surgery-related stress, postoperative complications, and the administration of adjuvant chemotherapy. Patients with a low pre-LMR and low post-LMR exhibited a similar lower 5-year OS rate to patients with a higher pre-LMR and low post-LMR in the present study. Patients with a high pre-LMR and high post-LMR had a similar high 5-year OS rate to patients with a low pre-LMR and high post-LMR. The LMR level at 12 months postoperatively determined the long-term survival of patients, but survival was not affected by pre-LMR levels. This did not mean that pre-LMR is not important. The accuracy of the 5-year overall survival predicted by the pre-LMR is only lower than the post-12-month LMR but higher than other time points post-LMR. These results indicate that pre-LMR has predictive value for prognosis in patients with GC for a period of up to 1 year. However, when the follow-up is up to 12th month after surgery, the LMR obtained by retesting of blood routine examination can replace the pre-LMR as an indicator of prognosis. These findings provide additional prognostic information that was not reported in previous studies examining the pre-LMR alone.13The second part of the study explored the effects of changes in preoperative and post-SIR levels on long-term survival. Because the LMR was the only CBC-based inflammatory marker that affected OS in the multivariate analysis and LMR predicted long-term survival with the highest accuracy at 12 months postoperatively, we combined the pre-LMR and post-12-month LMR to explore the prognostic value of changes in SIR levels. According to X-tile software, the optimal cutoff value for the pre-LMR was 3.42, and the optimal cutoff value for the post-12-month LMR was 4.00. Vincent et al. investigated risk factors for predicting the early recurrence of pancreatic ductal adenocarcinoma. The best predictive cutoff value for preoperative CA19-9 was 210 U/ml, and the optimal cutoff value for postoperative CA-199 was 37 U/ml.17 Combined with the findings of this study, we obtained a modified predictive model that was established by incorporating the LMR level at 12 months postoperatively into the TNM staging system, which had a higher AUC than the TNM staging system (P = 0.003). Thus, the LMR recorded at 12 months after surgery predicts survival independent of the TNM staging system, and its combined application with the TNM staging system improves the prognostic accuracy.The TNM staging system is a standard method for staging GC used by clinicians and medical workers. It is mainly used for an evaluation of the postoperative prognosis and follow-up treatment decision making processes.Our study has some limitations. First, this study is a single-center exploratory study that lacks external validation. Second, because not all patients are scheduled for blood tests after surgery, fewer results were available at 12 months postoperatively. Nevertheless, this study is the first to describe the changes in perioperative SIR levels in patients with GC and to determine their prognostic value. These findings may serve as bases for further prospective studies and may ultimately affect the follow-up strategies for GC patients.ESM 1(PDF 85 kb)ESM 2(DOCX 17 kb)ESM 3(DOCX 18 kb)ESM 4(DOCX 21 kb)ESM 5(DOCX 15 kb)